Combating Packet Collisions Using Non-Stationary Signal Scaling in LPWANs

LoRa, a representative Low-Power Wide Area Network (LPWAN) technology, has been shown as a promising platform to connect Internet of Things. Practical LoRa deployments, however, suffer from collisions, especially in dense networks and wide coverage areas expected by LoRa applications. Existing colli...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/ACM transactions on networking 2022-06, Vol.30 (3), p.1-14
Main Authors: Tong, Shuai, Wang, Jiliang, Liu, Yunhao
Format: Article
Language:English
Subjects:
Chirp
collision resolving
Collisions
Decoding
Demodulation
Frequency modulation
Internet of Things
Iterative methods
LoRa
LPWAN
Offsets
parallel transmission
Scaling
Signal to noise ratio
Time-frequency analysis
Wide area networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:LoRa, a representative Low-Power Wide Area Network (LPWAN) technology, has been shown as a promising platform to connect Internet of Things. Practical LoRa deployments, however, suffer from collisions, especially in dense networks and wide coverage areas expected by LoRa applications. Existing collision resolving approaches do not exploit the modulation properties of LoRa and thus cannot work well for low-SNR LoRa signals. We propose NScale to decompose concurrent transmissions by leveraging subtle inter-packet time offsets for low SNR LoRa collisions. NScale (1) translates subtle time offsets, which are vulnerable to noise, to robust frequency features, and (2) further amplifies the time offsets by non-stationary signal scaling, i.e., scaling the amplitude of a symbol differently at different positions. In practical implementation, we propose a noise resistant iterative symbol recovery method to combat symbol distortion in low SNR, and address frequency shifts incurred by CFO and packet time offsets in decoding. We propose optimized designs for diminishing the time costs of computation-intensive tasks and meeting the real-time requirements of LoRa collision resolving. We theoretically show that NScale introduces
ISSN:1063-6692
1558-2566
DOI:10.1109/TNET.2021.3131704